CASE STUDY

The Project

A university lab's research involved exploring the potential of RNAi in the nucleus and its effects on transcription, enhancer elements, and splicing, which could open up new gene targets. To analyze the effect of increasing cell density on gene expression and RNA splicing, they required an efficient workflow and prioritization of genes before conducting bench science.

The Problem

Dependent on external bioinformatics resources to develop their secondary analysis workflows, this team of scientists was encountering issues including:

Inaccessibility

• Communication barriers and competing priorities made working with an external bioinformatician a time-consuming and generally frustrating experience for the lab.
  

• Contributing to and understanding traditional bioinformatics analyses were challenging for the team due to:
  • Limited experience in coding and stats
  • Difficulty managing large, complex data sets
  • Constantly evolving bioinformatics tools
  • Cross-functional translation barriers
  • Limited access to high-performance computing

Lack of Reproducibility

• Due to this inaccessibility, the lab lacked documentation and clear methodologies of their previous analyses, which hindered their understanding and made it difficult to repeat studies.

• Previous pipelines were subject to software changes that could influence different results, which also complicated study repetition.

Inefficient Use of Resources

• Although an integral part of the research and discovery process, waiting on external bioinformatics analyses was a strain on the lab’s budget, the students’ time and the team’s energy.

• With a focus on hypothesis-generating results, the lab required a faster process with a strong scientific foundation that would enable them to have the confidence in their results to use these resources to lead to worthwhile discovery.

The Results

Almaden Genomics introduced the lab to g.nome^®, a cloud-native platform designed to streamline genomic workflows with pre-built workflows and a library of open-source tools.

“Easy enough for a biologist to use” – without a bioinformatician

• Lab members used pre-built workflows for alternative splicing analysis, with the ability to customize any aspect of the workflow as needed.

• The platform’s user-friendly graphical interface allows drag-and-drop functionality, transparency, and intuitive analysis for the bench scientists who could not code themselves.

Transparent Workflows Led to Reproducible Results

• Containerization within pipelines in g.nome provided version control and stability for each lab member, regardless of the computer or system being used.

• The platform’s comprehensive logging of tool versions, functions, parameters, etc. ensured the team could trace and reproduce their results.

Quality Results Accelerated Iteration

• This team managed to complete their pipeline and analyze the data within just TWO WEEKS of signing up with g.nome. The lab used the platform to analyze RNA sequencing data obtained from cells grown at different densities, narrow down biologically significant events, and identify candidate binding sites for miRNAs - an otherwise highly time-consuming process, especially if they had to rely on an external bioinformatician.

• These findings enabled the team to pivot and iterate as needed in order to achieve valuable insights to further influence drug discovery and development.

Conclusion

g.nome's low/no-code environment paired with a pre-built workflow and open-source tools made it possible for this team of biologists to not only assemble and run a data analysis pipeline on their own, but also complete it in matter of only two weeks. Containerization and comprehensive logging native to the platform will also ensure that the lab can easily reproduce their results and apply their learnings to future research.